Michael Monnerie wrote:
On Samstag, 29. Oktober 2005 06:33 Linda Walsh wrote:
Assuming it is some sort of berkeley db format, what is a good
cut-over size as a "rule-of-thumb"...or is there? What should I
expect in speeds for "sa-learn" or spamc? I.e. -- is there a
rough guideline for when it becomes more effective to use SQL
vs. the Berkeley DB? Or rephrased, when it is worth the effort to
convert to SQL and ensure all the SQL software is setup and running?
I don't know whether this really is a performance question, but I
believe it's more of a "do I need it" question. For example, if you use
a system wide bayes db, you probably won't need SQL. I do this for now.
---
Still am not sure what size system (or user) db's should trigger
usage of "SQL". Any reason why user DB's would hurt performance
over a system DB using Berkeley format? Supposing I have no system
DB and am only using user DB's? What if it is a small group 3-4 people?
Is it an issue of having to read in the DB with each email / user and
the system DB might hang around in memory? Does the system DB get some
preferential treatment? I.e. if one user gets 80% of the email, will
SA operate as though it is using a system DB?
Still not so sure about why "sa-learn" would process emails so much
more slowly than 2.6x, since for an individual user, it wouldn't be
accessing a system DB, no?
But if some users want/need their own bayes, or own settings, it starts
becoming easier to use SQL for all that things - it's quickly becoming
easier to manage, after 5 users or so need their special config. That's
why I'm thinking of switching to SQL.
Does anybody know whether MySQL or PostgreSQL is better suited for the
job? I prefer PostgreSQL, but many times MySQL is better supported...
mfg zmi
